Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results Appendix The appendix is structured in four sections. A. Proofs on differential […]

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arXiv:2603.15812v1 Announce Type: new Abstract: Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time. Recent end-to-end approaches address this by jointly learning 2D Bird’s Eye View (BEV) representations and identity associations, achieving high tracking accuracy. However, these methods offer no principled uncertainty accounting and remain tightly coupled to their training configuration, limiting generalization across […]

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Key Highlights: After demand from China soared high, followed by some leniency in the H200 chip export rule by the Trump administration, NVIDIA has once again found itself in a position where it has to adjust its AI chip strategy. However, the approval from the U.S. government isn’t straight-forward. The Trump administration has set a condition of 25% fee on each sale.  After Trump eases China export rules, NVIDIA plans to ram-up production of H200 AI chips Last […]

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arXiv:2408.06710v3 Announce Type: replace-cross Abstract: Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach is primarily limited to analyzing simple data structures, as the generation of an effective proposal distribution can become quite challenging in high-dimensional spaces […]

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In a traditional monastery the recitation of the Hours calls to mind the work of God in our lives, from the gift of creation to the sufferings we bear. According to scientist, theologian, and author Ilia Delio, the universe is the new monastery. In her latest work she offers reflections for this new monastery to a broad, general audience seeking new meaning and purpose in today’s world.

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arXiv:2603.09985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their ability to accurately assess their own confidence remains poorly understood. We present an empirical study investigating whether LLMs exhibit patterns reminiscent of the Dunning-Kruger effect — a cognitive bias where individuals with limited competence tend to overestimate their abilities. We evaluate four state-of-the-art models (Claude Haiku 4.5, Gemini 2.5 Pro, Gemini 2.5 Flash, and Kimi K2) across four benchmark datasets […]

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OpenAI has just launched Frontier. It is a new platform that promises to help enterprises build, deploy, and manage AI agents that do real work. With this platform, OpenAI is looking to bridge the gap between what AI models can do and what businesses can actually use in production. Well, it’s not hidden anymore that AI has been playing a massive role inside an organization. Things that were hard to tackle by teams have now become a breeze, […]

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Estimating the Riesz representer is central to debiased machine learning for causal and structural parameter estimation. We propose generalized Riesz regression, a unified framework that estimates the Riesz representer by fitting a representer model via Bregman divergence minimization. This framework includes the squared loss and the Kullback–Leibler (KL) divergence as special cases: the former recovers Riesz regression, while the latter recovers tailored loss minimization. Under suitable model specifications, the dual problems correspond to covariate balancing, which we call […]

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